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uTUG: An unsupervised Timed Up and Go test for Parkinson's disease
João Elison da Rosa Tavares
*
, Martin Ullrich
, Nils Roth
, Felix Kluge
, Bjoern M. Eskofier
, Heiko Gaßner
, Jochen Klucken
, Till Gladow
, Franz Marxreiter
, Cristiano André da Costa
, Rodrigo da Rosa Righi
, Jorge Luis Victória Barbosa
*
Corresponding author for this work
Digital Medicine
Research output
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Contribution to journal
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Article
›
Research
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peer-review
12
Citations (Scopus)
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Dive into the research topics of 'uTUG: An unsupervised Timed Up and Go test for Parkinson's disease'. Together they form a unique fingerprint.
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Keyphrases
Parkinson's Disease
100%
Timed-up-and-go Test
100%
Gait Test
50%
Parkinson Patients
33%
Gait Data
33%
Inertial Measurement Unit
33%
Random Forest
33%
Timed-up-and-go
33%
Manual Annotation
33%
Real-world Gait
33%
Disease Progression
16%
Fall Risk
16%
Gait Parameters
16%
Support Vector Machine
16%
Automatic Classification
16%
Motion Parameters
16%
Machine Learning
16%
Classification Algorithms
16%
Movement Analysis
16%
Recording System
16%
Clinical Gait
16%
Movement Monitoring
16%
Test Detection
16%
Automatic Detection
16%
Patient Monitoring
16%
Nave Bayes Classifier
16%
Foot-mounted
16%
Present Challenges
16%
Additional Markers
16%
Automatic Decomposition
16%
Reference Measurements
16%
Learning Support
16%
Clinical Visit
16%
Biomechanical Motion
16%
Pre-filtering
16%
Data Context
16%
Monitoring Protocol
16%
World Data
16%
Patient Falls
16%
Fl Score
16%
Computer Science
Parkinson's Disease
100%
Disease Patient
66%
Annotation
66%
Random Decision Forest
66%
Automatic Detection
33%
Disease Progression
33%
Automatic Classification
33%
Nave Bayes
33%
Bayes Classifier
33%
Machine Learning
33%
Learning System
33%
Medicine and Dentistry
Parkinson's Disease
100%
Gait
100%
Timed Up and Go Test
100%
Clinician
33%
Patient Monitoring
16%
Disease Exacerbation
16%
Movement Analysis
16%
Real-World Data
16%
Nursing and Health Professions
Parkinson's Disease
100%
Timed Up and Go Test
100%
Random Forest
33%
Disease Exacerbation
16%
Patient Monitoring
16%
Support Vector Machine
16%
Neuroscience
Parkinson's Disease
100%
Gait
100%
Support Vector Machine
16%
Movement Analysis
16%
Pharmacology, Toxicology and Pharmaceutical Science
Parkinson's Disease
100%
Disease Exacerbation
33%